Developing An OpenEarth Framework (OEF)

The Geosciences Network (GEON) brought together 16 institutions to develop an infrastructure for managing large distributed data collections. In the next generation of that work, the OpenEarth Framework is expanding this infrastructure with open source software for integrating, analyzing, and visualizing these multidisciplinary datasets.

Based upon community-driven open standards for data models and services, this work is integrating data with different data types, formats, storage schemes, dimensionality, and coordinate spaces. The layered OEF software stack extends from deep within web-available data archives outwards to interactive visualization tools running on the user's desktop or laptop computer.

At the lowest level, Dataset Access Services manage and deliver stored data and metadata. These services hide storage details, such as the storage medium, access authentication, data replication, and storage optimization. Stored metadata characterizes registered data, including its spatial and temporal extent, resolution, and history.

At the next higher level, Data Modeling Services provide operations to subset, extract, analyze, and derive data of interest. These services respond to on-demand web queries and use recent access patterns to guide pre-processing to prepare data in anticipation of future need.

Above this level, Data Interaction Services include operations to grid data, resample it at multiple resolutions, and stage it to enable a rapid response to data queries supporting user interaction.

Finally, the OEF's Visualization Tools run on the user's computer, query spatial and temporal data from the OEF services, and use 3D graphics hardware for fast display. Visualizations integrate data of different types to create a composite view, such as LiDAR surface elevations, satellite images, bore hole paths, and seismic tomography.

The OEF's open source approach encourages community participation and contribution to create a lasting community asset for exploring rich data archives and testing new data models, analysis algorithms, and visualization ideas.